RQ (_Redis Queue_) is a simple Python library for queueing jobs and processing
them in the background with workers.  It is backed by Redis and it is designed
to have a low barrier to entry.  It should be integrated in your web stack
easily.

RQ requires Redis >= 3.0.0.

[![Build status](https://github.com/rq/rq/workflows/Test%20rq/badge.svg)](https://github.com/rq/rq/actions?query=workflow%3A%22Test+rq%22)
[![PyPI](https://img.shields.io/pypi/pyversions/rq.svg)](https://pypi.python.org/pypi/rq)
[![Coverage](https://codecov.io/gh/rq/rq/branch/master/graph/badge.svg)](https://codecov.io/gh/rq/rq)
[![Code style: Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)


Full documentation can be found [here][d].


## Support RQ

If you find RQ useful, please consider supporting this project via [Tidelift](https://tidelift.com/subscription/pkg/pypi-rq?utm_source=pypi-rq&utm_medium=referral&utm_campaign=readme).


## Getting started

First, run a Redis server, of course:

```console
$ redis-server
```

To put jobs on queues, you don't have to do anything special, just define
your typically lengthy or blocking function:

```python
import requests

def count_words_at_url(url):
    """Just an example function that's called async."""
    resp = requests.get(url)
    return len(resp.text.split())
```

Then, create an RQ queue:

```python
from redis import Redis
from rq import Queue

queue = Queue(connection=Redis())
```

And enqueue the function call:

```python
from my_module import count_words_at_url
job = queue.enqueue(count_words_at_url, 'http://nvie.com')
```

Scheduling jobs are also similarly easy:

```python
# Schedule job to run at 9:15, October 10th
job = queue.enqueue_at(datetime(2019, 10, 10, 9, 15), say_hello)

# Schedule job to run in 10 seconds
job = queue.enqueue_in(timedelta(seconds=10), say_hello)
```

Retrying failed jobs is also supported:

```python
from rq import Retry

# Retry up to 3 times, failed job will be requeued immediately
queue.enqueue(say_hello, retry=Retry(max=3))

# Retry up to 3 times, with configurable intervals between retries
queue.enqueue(say_hello, retry=Retry(max=3, interval=[10, 30, 60]))
```

For a more complete example, refer to the [docs][d].  But this is the essence.


### The worker

To start executing enqueued function calls in the background, start a worker
from your project's directory:

```console
$ rq worker --with-scheduler
*** Listening for work on default
Got count_words_at_url('http://nvie.com') from default
Job result = 818
*** Listening for work on default
```

That's about it.


## Installation

Simply use the following command to install the latest released version:

    pip install rq

If you want the cutting edge version (that may well be broken), use this:

    pip install git+https://github.com/rq/rq.git@master#egg=rq


## Docs

To build and run the docs, install [jekyll](https://jekyllrb.com/docs/) and run:

```shell
cd docs
jekyll serve
```

## Related Projects

If you use RQ, Check out these below repos which might be useful in your rq based project.

- [django-rq](https://github.com/rq/django-rq)
- [rq-dashboard](https://github.com/Parallels/rq-dashboard)
- [rqmonitor](https://github.com/pranavgupta1234/rqmonitor)
- [Flask-RQ2](https://github.com/rq/Flask-RQ2)
- [rq-scheduler](https://github.com/rq/rq-scheduler)
- [rq-dashboard-fastAPI](https://github.com/Hannes221/rq-dashboard-fast)



## Project history

This project has been inspired by the good parts of [Celery][1], [Resque][2]
and [this snippet][3], and has been created as a lightweight alternative to the
heaviness of Celery or other AMQP-based queueing implementations.


[d]: http://python-rq.org/
[m]: http://pypi.python.org/pypi/mailer
[p]: http://docs.python.org/library/pickle.html
[1]: http://docs.celeryq.dev/
[2]: https://github.com/resque/resque
[3]: https://github.com/fengsp/flask-snippets/blob/1f65833a4291c5b833b195a09c365aa815baea4e/utilities/rq.py